KANs applied to GMSC credit data produce a small AUC gain over borrowed baseline numbers, with interpretability shown only through the model's own attribution scores.
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
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KACDP: A Highly Interpretable Credit Default Prediction Model
KANs applied to GMSC credit data produce a small AUC gain over borrowed baseline numbers, with interpretability shown only through the model's own attribution scores.